1.5.3_Seurat_Integration.Rmd

---
title: "1.5.3 Seurat integration"
author: "Vincent Gardeux"
date: "2023/12/22"
output:
  html_document:
    df_print: paged
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
knitr::opts_knit$set(root.dir = "./")
getwd()
```

## Libraries & functions

First, I'm loading the required libraries & functions

```{r}
suppressPackageStartupMessages(library(Seurat)) # For handling 10x h5 file
suppressPackageStartupMessages(library(crayon)) # Just for bolding the console output :D

cat(bold("Seurat"), "version", as.character(packageVersion("Seurat")), "\n")
```

### Integrate them all

#### Loading the list of all merged exps from previous step
```{r}
data.seurat_all <- readRDS("results/C3H10_10X_all_exps_merged.rds")
```

#### Now performing the integration (this takes a while) ~22mn for FindIntegrationAnchors

```{r}
# First, Normalize and HVG on each Seurat object
data.seurat_for_mnn <- list()
for(exp in names(data.seurat_all)){
  data.seurat <- data.seurat_all[[exp]]
  data.seurat$batch <- exp
  data.seurat <- NormalizeData(data.seurat, normalization.method = "LogNormalize", scale.factor = 10000, verbose = F)
  data.seurat_for_mnn[[exp]] <- FindVariableFeatures(data.seurat, selection.method = "vst", nfeatures = 2000, verbose = F)
}

# Then running the Seurat integration
int_features <- SelectIntegrationFeatures(object.list = data.seurat_for_mnn, verbose = F)
int_anchors <- FindIntegrationAnchors(object.list = data.seurat_for_mnn, anchor.features = int_features, verbose = F)
data.seurat_integrated <- IntegrateData(anchorset = int_anchors, verbose = F)

# Dimensions
DefaultAssay(data.seurat_integrated) <- "RNA"
dim(data.seurat_integrated)

# Run the default Seurat pipeline on the integrated object
data.seurat_integrated <- ScaleData(data.seurat_integrated, assay = "integrated", verbose = F)
data.seurat_integrated <- RunPCA(data.seurat_integrated, assay = "integrated", npcs = 200, verbose = F)
data.seurat_integrated <- RunTSNE(data.seurat_integrated, reduction = "pca", dims = 1:200)
data.seurat_integrated <- RunUMAP(data.seurat_integrated, reduction = "pca", dims = 1:200)
data.seurat_integrated <- SetIdent(data.seurat_integrated, value = "TF")

# Save Integrated Seurat object
saveRDS(data.seurat_integrated, "results/C3H10_10X_all_exps_merged_genefiltered_integrated.rds")
```

```{r}
DimPlot(data.seurat_integrated, reduction = "tsne") + NoLegend()
```

```{r}
DimPlot(data.seurat_integrated, reduction = "umap") + NoLegend()
```

```{r}
DimPlot(data.seurat_integrated, reduction = "umap", group.by = "batch")
```